CN111274290A - Examination arrangement system and method based on multidimensional data analysis - Google Patents
Examination arrangement system and method based on multidimensional data analysis Download PDFInfo
- Publication number
- CN111274290A CN111274290A CN202010058368.9A CN202010058368A CN111274290A CN 111274290 A CN111274290 A CN 111274290A CN 202010058368 A CN202010058368 A CN 202010058368A CN 111274290 A CN111274290 A CN 111274290A
- Authority
- CN
- China
- Prior art keywords
- data
- traversal
- examinee
- dimension
- label
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2465—Query processing support for facilitating data mining operations in structured databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/283—Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
- G06Q50/205—Education administration or guidance
Abstract
The invention belongs to the technical field of computers, and particularly relates to an examination arrangement system based on multidimensional data analysis, which comprises: the data acquisition unit is used for acquiring examinee data; the data analysis unit is used for carrying out multi-dimensional analysis on the examinee data, establishing a multi-dimensional label set aiming at each examinee data and simultaneously establishing data affinity among each dimension label in the multi-dimensional label set; the examination arrangement unit is used for carrying out random examination arrangement on the examinee data; the data processing unit is used for analyzing the normalized data affinity between each dimension label corresponding to each examinee data and each dimension label corresponding to adjacent examinee data according to the random examination arrangement result; by the examination arrangement, the occurrence of examination cheating conditions is reduced; meanwhile, the distribution efficiency and the accuracy are high.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to an examination arrangement system and method based on multidimensional data analysis.
Background
Examination is a strict knowledge level identification method. The learning ability and other abilities of the student can be checked through examination. In order to ensure the fairness of results, the examination hall must have strong discipline constraint, and is specially provided with the examination monitoring processes of primary examination, invigilation and the like, and absolutely forbids any cheating behavior, otherwise, the examination hall will undertake legal and criminal responsibility.
The examination is that a group of people with different education resources completes one same answer sheet in a certain time. However, the meaning of the examination is not limited to this, and the examination may be an all-round examination of a person for one target. Such an examination is in fact a chance to change itself for people from different social positions in the society.
Cheating on examination is a major principle problem and is also a moral problem. Examination cheating is also a negative resistance to a rigidizing educational system. The phenomenon of cheating is widespread, but widespread does not mean that it is a matter of course. The cheating is not respected by the cheating person and others. It is not fair to other people who pay with hearts. May be successful occasionally, giving the cheater great spiritual and physical encouragement, and further stimulating and confirming the confidence and the decision that the group carries out the cheating. However, the time is never a long time, and the time may give some gap and opportunity for survival to the speculator and the skimming persons, but the time is not always exhaustive, and the time can lead the work result of others to be private for themselves, and the only function of the pirates is just the copy. There is no benefit to itself.
The examination arrangement can stop the occurrence of examination cheating to a great extent, if the examination arrangement is carried out, the most reasonable arrangement is made according to the personal information of the examinees, so that the examinees can be ensured to be difficult to cheat through the adjacent examinees, and the examination cheating can be avoided to a certain extent.
Disclosure of Invention
The invention mainly aims to provide an examination arrangement system and method based on multidimensional data analysis, which can reduce the occurrence of examination cheating conditions through examination arrangement; meanwhile, the distribution efficiency and the accuracy are high.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
a system for scheduling examinations based on multidimensional data analysis, comprising: the data acquisition unit is used for acquiring examinee data; the data analysis unit is used for carrying out multi-dimensional analysis on the examinee data, establishing a multi-dimensional label set aiming at each examinee data and simultaneously establishing data affinity among each dimension label in the multi-dimensional label set; the examination arrangement unit is used for carrying out random examination arrangement on the examinee data; the data processing unit is used for analyzing the normalized data affinity between each dimension label corresponding to each examinee data and each dimension label corresponding to adjacent examinee data according to the random examination arrangement result, adjusting the examinees with the data affinity higher than a set threshold value, and traversing the whole random examination arrangement result until the data affinity between each examinee data and the adjacent examinee data is below the set threshold value; the data analysis unit is used for carrying out multi-dimensional analysis on the examinee data, and the method for establishing the multi-dimensional label set aiming at each examinee data executes the following steps: dividing the data of each examinee into t dimensions, and calculating the clustering center of the data of each examinee according to the following formula:
wherein t is the number of dimensions, c is the number of clusters, N is the total number of samples, UtAn affiliation matrix, V, representing the t-th dimensiontDenotes the cluster center in the t-th dimension, XtRepresents the t-th dimensionally small cluster sample,
represents the center point of the ith class in the t-th dimension, d is the dimension number of the sample, xj，tDenotes the jth sample point, μ, in the t dimensionij，tRepresenting the membership degree of the jth sample belonging to the ith class under the t dimensionality, wherein m is an adjustment coefficient and must satisfy m<1: according to the center of the cluster that is established,
is a clustering center; calculating by means of the established cluster centersThe distance of each dimension label from the clustering center; obtaining a coordinate point of the dimension label according to the calculated distance; and forming a set by all the obtained coordinate points to be used as a multi-dimensional label set.
Further, the data analysis unit, the method for establishing data affinity between each dimension label in the multi-dimension label set, executes the following steps: setting the distance transformation function of each dimension label as:
Further, the data processing unit, according to the random examination arrangement result, performs the following steps by the method of analyzing the normalized data affinity between each dimension label corresponding to each examinee data and each dimension label corresponding to the adjacent examinee data: setting the coordinates of target examinee data as follows: (x'n，y′n) (ii) a Regarding the point as a point in the chaotic system, and obtaining a chaotic mapping equation of the point as follows:
xn＝a+C·x′n
yn＝b+d·y′n(ii) a Wherein x isnAnd ynThe abscissa and ordinate of the examinee data for the next analytical calculation; x'nAnd y'nThe abscissa and the ordinate of the examinee data subjected to analysis and calculation at present are taken as the reference; a is a first adjustment parameter, and the range is: (1-5); c is a first adjustment coefficient in the range of (1.1-1.5); b is a second adjustment parameter, the range is: (1-5); d is a second adjustment coefficient in the range of (1.1-1.5).
Further, the method of the data processing unit traversing the entire random exam arrangement results performs the steps of: setting a violence traversal rule of the random examination arrangement set, taking the violence traversal rule and the random examination arrangement set as data sources of a traversal algorithm, and constructing numbers in the password set by using the traversal algorithm to generate a final traversal random examination arrangement set; the random examination arrangement set comprises a first traversal digital table and a second traversal digital table, the first traversal digital table and the second traversal digital table are both 4 rows and 13 columns, the first traversal digital table stores the numbers after being generated simply according to the random examination arrangement set rule, the second traversal digital table stores the numbers after being generated according to the random examination arrangement set rule, the 1 st row in the first traversal digital table and the 1 st row in the second traversal digital table are stored in a top grid mode, and the 2 nd row to the 4 th row are stored in a blank grid mode according to the random examination arrangement set digital rule.
Further, the violence traversal rule of the first traversal number table and the second traversal number table includes: locking a certain row in the digital history table to traverse from left to right, from right to left, from left to right and back again, and from right to left and back again; traversing the rule from top to bottom, locking a certain column in the digital history table to go from top to bottom, from bottom to top, and then returning from top to bottom, and then returning to the rule from bottom to top; the cross-row traversal rule is used for locking some rows in the digital traversal table to regularly traverse in the same direction and the opposite direction of each row; and traversing the rule in a cross-column manner, wherein a certain column in the locking number traversal table is traversed by the rule that each column is in the same direction and each column is in the opposite direction.
A method for scheduling examinations based on multidimensional data analysis, said method performing the steps of:
step 1: step 1: used for collecting the examinee data;
step 2: carrying out multi-dimensional analysis on the examinee data, establishing a multi-dimensional label set aiming at each examinee data, and simultaneously establishing data affinity among each dimension label in the multi-dimensional label set;
and step 3: carrying out random examination arrangement on the examinee data;
and 4, step 4: and analyzing the normalized data affinity between each dimension label corresponding to each examinee data and each dimension label corresponding to adjacent examinee data according to the random examination arrangement result, adjusting the examinees with the data affinity higher than a set threshold value, and traversing the whole random examination arrangement result until the data affinity between each examinee data and the adjacent examinee data is below the set threshold value.
Further, the data analysis unit performs multidimensional analysis on the examinee data, and the method for establishing the multidimensional label set for each examinee data performs the following steps: dividing the data of each examinee into t dimensions, and calculating the clustering center of the data of each examinee according to the following formula:
wherein t is the number of dimensions, c is the number of clusters, N is the total number of samples, UtRepresenting a membership matrix, V, in the t-th dimensiontDenotes the cluster center in the t-th dimension, XtRepresents the t-th dimensionally small cluster sample,
represents the center point of the ith class in the t-th dimension, d is the dimension number of the sample, xj，tDenotes the jth sample point, μ, in the t dimensionij，tRepresenting the membership degree of the jth sample belonging to the ith class under the t dimensionality, wherein m is an adjustment coefficient and must satisfy m<1: based on the established center of the cluster, the cluster center,
is the center of the cluster; calculating the distance between each dimension label and the clustering center through the established clustering center; obtaining a coordinate point of the dimension label according to the calculated distance; and forming a set by all the obtained coordinate points to be used as a multi-dimensional label set.
Further, the data analysis unit, the method for establishing data affinity between each dimension label in the multi-dimension label set, executes the following steps: setting the distance transformation function of each dimension label as:
Further, the data processing unit, according to the random examination arrangement result, performs the following steps by the method of analyzing the normalized data affinity between each dimension label corresponding to each examinee data and each dimension label corresponding to the adjacent examinee data: setting the coordinates of target examinee data as follows: (x'n，，y′n) (ii) a Regarding the point as a point in the chaotic system, and obtaining a chaotic mapping equation of the point as follows:
Further, the method of the data processing unit traversing the entire random exam arrangement results performs the steps of: setting a violence traversal rule of the random examination arrangement set, taking the violence traversal rule and the random examination arrangement set as data sources of a traversal algorithm, and constructing numbers in the password set by using the traversal algorithm to generate a final traversal random examination arrangement set; the random examination arrangement set comprises a first traversal digital table and a second traversal digital table, the first traversal digital table and the second traversal digital table are both 4 rows and 13 columns, the first traversal digital table stores the numbers after being generated simply according to the random examination arrangement set rule, the second traversal digital table stores the numbers after being generated according to the random examination arrangement set rule, the 1 st row in the first traversal digital table and the 1 st row in the second traversal digital table are stored in a top grid mode, and the 2 nd row to the 4 th row are stored in a blank grid mode according to the random examination arrangement set digital rule.
The examination arrangement system and method based on multidimensional data analysis have the following beneficial effects: the method comprises the steps of performing multi-dimensional numerical analysis on the data of the examinees, establishing a multi-dimensional label set aiming at each examinee data, and simultaneously establishing data affinity between each dimension label in the multi-dimensional label set; and then, the portrait of each examinee is constructed in the examination environment, the social relationship between the examinee adjacent to the examinee and the examinee can be analyzed according to the constructed portrait of each examinee, and the condition that two persons with relatively close social relationships are adjacent to each other and sit can be avoided according to the analyzed social relationship, so that examination cheating occurs. Meanwhile, when data analysis and processing are carried out, a new algorithm is used for processing, so that the analysis result is more accurate. On the basis of the analysis result, a new traversal algorithm is used, so that the traversal efficiency is higher, and the final result is realized more quickly.
Drawings
Fig. 1 is a schematic system structure diagram of a multidimensional data analysis based examination scheduling system according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating a method of scheduling an examination based on multidimensional data analysis according to an embodiment of the present invention;
fig. 3 is a comparative experimental graph of a processing efficiency experimental curve diagram during data processing and a processing efficiency experimental curve diagram of the prior art of the examination scheduling system and method based on multidimensional data analysis according to the embodiment of the present invention;
fig. 4 is an experimental graph comparing an experimental graph of cheating rate after test arrangement of the system and method for arranging tests based on multidimensional data analysis according to the embodiment of the present invention with an experimental graph of the prior art.
1-experimental curve schematic diagram of the invention, 2-experimental curve schematic diagram of the prior art.
Detailed Description
The method of the present invention will be described in further detail below with reference to the accompanying drawings and embodiments of the invention.
Example 1
As shown in fig. 1, a system for scheduling examinations based on multidimensional data analysis includes: the data acquisition unit is used for acquiring examinee data; the data analysis unit is used for carrying out multi-dimensional analysis on the examinee data, establishing a multi-dimensional label set aiming at each examinee data and simultaneously establishing data affinity among each dimension label in the multi-dimensional label set; the examination arrangement unit is used for carrying out random examination arrangement on the examinee data; the data processing unit is used for analyzing the normalized data affinity between each dimension label corresponding to each examinee data and each dimension label corresponding to adjacent examinee data according to the random examination arrangement result, adjusting the examinees with the data affinity higher than a set threshold value, and traversing the whole random examination arrangement result until the data affinity between each examinee data and the adjacent examinee data is below the set threshold value; the method is characterized in that the data analysis unit carries out multi-dimensional analysis on examinee data, and the method for establishing the multi-dimensional label set aiming at each examinee data executes the following steps: dividing the data of each examinee into t dimensions, and calculating the clustering center of the data of each examinee according to the following formula:
wherein t is the number of dimensions, c is the number of clusters, N is the total number of samples, UtAn affiliation matrix, V, representing the t-th dimensiontDenotes the cluster center in the t-th dimension, XtRepresents the t-th dimensionally small cluster sample,
represents the center point of the ith class in the t-th dimension, d is the dimension number of the sample, xj，tDenotes the jth sample point, μ, in the t dimensionij，tRepresenting the membership degree of the jth sample belonging to the ith class under the t dimensionality, wherein m is an adjustment coefficient and must satisfy m<1: according to the center of the cluster that is established,
is a clustering center; calculating the distance between each dimension label and the clustering center through the established clustering center; obtaining a coordinate point of the dimension label according to the calculated distance; and forming a set by all the obtained coordinate points to be used as a multi-dimensional label set.
By adopting the technical scheme, the method comprises the steps of carrying out multi-dimensional number analysis on the examinee data, establishing a multi-dimensional label set aiming at each examinee data, and simultaneously establishing data affinity among each dimension label in the multi-dimensional label set; and then, the portrait of each examinee is constructed in the examination environment, the social relationship between the examinee adjacent to the examinee and the examinee can be analyzed according to the constructed portrait of each examinee, and the condition that two persons with relatively close social relationships are adjacent to each other and sit can be avoided according to the analyzed social relationship, so that examination cheating occurs. Meanwhile, when data analysis and processing are carried out, a new algorithm is used for processing, so that the analysis result is more accurate. On the basis of the analysis result, a new traversal algorithm is used, so that traversal efficiency is higher, and the final result is realized more quickly.
Specifically, the dimension is a kind of data, and the social relationship may include: colleges and universities, age, sex, residence and the like. Each type of information represents a dimension of data. The number of dimensions will affect the accuracy of the analysis, and the greater the number of dimensions, the more accurate the analysis, but the less efficient its analysis will be accordingly.
Example 2
On the basis of the above embodiment, the data analysis unit, the method for establishing the data affinity between each dimension label in the multi-dimension label set, performs the following steps: setting the distance transformation function of each dimension label as:
where D (p, q) represents a set of Euclidean distances for each dimension label, dimension label Da(p) ordinate, D, of dimension labelb(q) denotes the abscissa of the dimension label, IbRepresenting a range value of the abscissa, wherein the value range is { 2-12 }; h isbAnd the range value of the vertical coordinate is { 3-15 }.
Specifically, through distance calculation of data dimensions, different data distances corresponding to each dimension can be analyzed, and then the affinity of the data distances is judged. A dimension is a measured environment and is used to reflect a class of attributes of a service, and a set of such attributes constitutes a dimension, which may also be referred to as an entity object. The dimensions belong to a data domain, such as geographic dimensions (including content on the level of country, region, province, and city), and temporal dimensions (including content on the level of year, season, month, week, day, etc.).
Dimensions are the basis and soul of the dimensional modeling. In dimensional modeling, a metric is referred to as a "fact," describing an environment as a "dimension," which is a diverse environment needed for analyzing facts. For example, when analyzing the transaction process, the environment in which the transaction occurs may be described by dimensions of buyer, seller, goods and time.
The columns that a dimension contains, which represent the dimension, are called dimension attributes. The dimension attributes are basic sources for query constraints, grouping and report tag generation, and are key to data usability.
Example 3
On the basis of the previous embodiment, the method for analyzing the normalized data affinity between each dimension label corresponding to each examinee data and each dimension label corresponding to adjacent examinee data according to the random examination arrangement result by the data processing unit executes the following steps: setting the coordinates of target examinee data as follows: (x'n，y′n) (ii) a Regarding the point as a point in the chaotic system, and obtaining the chaotic mapping equation of the point as follows:
and then obtaining a sequential equation of the target examinee data and other adjacent examinee data for analysis and calculation through the following formula:
xn＝a+C·x′n
yn＝b+d·y′n(ii) a Wherein x isnAnd ynThe abscissa and ordinate of the examinee data for the next analytical calculation; x'nAnd y'nThe abscissa and the ordinate of the examinee data subjected to analysis and calculation at present are taken as the reference; a is a first adjustment parameter, and the range is: (1-5); c is a first adjustment coefficient in the range of (1.1-1.5); b is a second adjustment parameter, the range is: (1-5); d is a second adjustment coefficient in the range of (1.1-1.5).
Specifically, the chaotic system refers to a deterministic system in which seemingly random irregular motion exists, and the behavior of the chaotic system is represented by uncertainty, unrepeatability and unpredictability, which is a chaotic phenomenon. Chaos is an inherent characteristic of a nonlinear power system, and is a phenomenon that a nonlinear system is ubiquitous. Chaos can be divided into four types according to the nature of the dynamical system: temporal chaos, spatial chaos, spatiotemporal chaos, functional chaos.
By adopting the technical scheme, the invention carries out the calculation sequence of the examinee data through the chaotic system, and the problem of how to determine the analysis and calculation sequence of the examinee data is difficult because the data difference of the examinee is not obvious. When the chaotic system determines the sequence, the whole system is regarded as chaotic, and when the data difference is not large, the traversal efficiency is highest.
Example 4
On the basis of the above embodiment, the method of the data processing unit traversing the entire random examination arrangement results performs the following steps: setting a violence traversal rule of the random examination arrangement set, taking the violence traversal rule and the random examination arrangement set as data sources of a traversal algorithm, and constructing numbers in the password set by using the traversal algorithm to generate a final traversal random examination arrangement set; the random examination arrangement set comprises a first traversal digital table and a second traversal digital table, wherein the first traversal digital table and the second traversal digital table are 13 rows and 4 columns, the first traversal digital table stores the simply generated numbers according to the random examination arrangement set rule, the second traversal digital table stores the generated numbers according to the random examination arrangement set rule, the 1 st row in the first traversal digital table and the 1 st row in the second traversal digital table are both stored, and the 1 st row to the 4 th row are both empty and the 1 st is stored according to the random examination arrangement set digital rule.
Specifically, the traversal algorithm is an important research direction in the field of computers, and the solution of a problem is a process of changing the current state from the initial state by using the existing rules and conditions until the current state is changed into the final target state, and connecting all the states appearing in the middle to form a traversal path. Through the traversal of the graph, we can find this path. The graph traversal algorithm mainly comprises two algorithms, wherein one algorithm is an algorithm for developing traversal according to a depth-first sequence, namely depth-first traversal; the other is an algorithm for developing traversal in breadth-first order, i.e., breadth-first traversal. Breadth-first traversal is a traversal of all nodes of the graph along the depth of the graph, each traversal traversing along the neighbors of the current node until all the traversal of all the nodes is completed. If all the neighbors of the current node have been traversed, the previous node is traced back and the process is repeated until all nodes reachable from the source node have been visited. If there are no more nodes to be accessed, one of the nodes is selected as the source node and the process is repeated until all nodes are accessed. By using the depth-first search of the graph, a lot of additional information can be obtained, and a lot of graph theory problems can be solved. Breadth-first traversal is also known as breadth-first traversal. By traversing the nodes of the graph along the width of the graph, the algorithm then terminates if all nodes are visited. Implementation of breadth-first traversal typically requires a queue to assist in completion. Breadth-first traversal is also a blind traversal method, as are depth-first traversal. That is, the breadth traversal algorithm does not use a rule-of-thumb algorithm, does not consider the possible addresses of the results, and simply traverses the entire graph thoroughly.
Example 5
On the basis of the above embodiment, the violence traversal rule of the first traversal number table and the second traversal number table includes: locking a certain row in the digital history table to traverse from left to right, from right to left, from left to right and back again, and from right to left and back again; traversing the rule from top to bottom, locking a certain column in the digital history table to traverse from top to bottom, from bottom to top, returning from top to bottom and returning to the rule from bottom to top; the cross-row traversal rule is used for locking some rows in the digital traversal table to regularly traverse in the same direction and the opposite direction of each row; and traversing the rule in a cross-column manner, wherein a certain column in the locking number traversal table is traversed by the rule that each column is in the same direction and each column is in the opposite direction.
Example 6
As shown in fig. 2, a multi-dimensional data analysis-based examination scheduling method performs the following steps:
step 1: step 1: used for collecting the examinee data;
step 2: carrying out multi-dimensional analysis on the examinee data, establishing a multi-dimensional label set aiming at each examinee data, and simultaneously establishing data affinity among each dimension label in the multi-dimensional label set;
and step 3: carrying out random examination arrangement on the examinee data;
and 4, step 4: and analyzing the normalized data affinity between each dimension label corresponding to each examinee data and each dimension label corresponding to adjacent examinee data according to the random examination arrangement result, adjusting the examinees with the data affinity higher than a set threshold value, and traversing the whole random examination arrangement result until the data affinity between each examinee data and the adjacent examinee data is below the set threshold value.
Specifically, the breadth-first traversal differs from the depth-first traversal in that: breadth-first traversal is to search for the next layer after all nodes on a certain layer are searched for in the order of layers; and the depth-first traversal is to search all nodes on one branch after all nodes on the other branch are searched.
The depth-first traversal starts from a certain vertex, firstly visits the vertex, then finds out the first un-visited neighbor node which just visits the node, then uses the neighbor node as the vertex, continues to find out the next new vertex for visiting, and repeats the steps until all the nodes are visited.
The breadth-first traversal starts from a certain vertex, firstly accesses the vertex, then finds out all the non-accessed adjacent points of the node, accesses all the nodes of the first adjacent point in the nodes after the access is finished, and repeats the method until all the nodes are accessed.
The biggest difference between the two methods can be seen in that the former method visits the first adjacent point of the vertex all the time and then visits the second adjacent point of the vertex; the latter starts from the vertex to visit all the adjacent points of the vertex and then successively goes down, one layer by one layer.
Example 7
Further, the data analysis unit performs multidimensional analysis on the examinee data, and the method for establishing the multidimensional label set for each examinee data performs the following steps: dividing the data of each examinee into t dimensions, and calculating the clustering center of the data of each examinee according to the following formula:
wherein t is the number of dimensions, c is the number of clusters, N is the total number of samples, UtRepresenting a membership matrix, V, in the t-th dimensiontDenotes the cluster center in the t-th dimension, XtRepresents the t-th dimensionally small cluster sample,
represents the center point of the ith class in the t-th dimension, d is the dimension number of the sample, xj，tDenotes the jth sample point, μ, in the t dimensionij，tRepresenting the membership degree of the jth sample belonging to the ith class under the t dimensionality, wherein m is an adjustment coefficient and must satisfy m<1: based on the established center of the cluster, the cluster center,
is the center of the cluster; calculating the distance between each dimension label and the clustering center through the established clustering center; obtaining a coordinate point of the dimension label according to the calculated distance; and forming a set by all the obtained coordinate points to be used as a multi-dimensional label set.
Specifically, clustering differs from classification in that the class into which the clustering is required to be classified is unknown.
Clustering is a process of classifying data into different classes or clusters, so that objects in the same cluster have great similarity, and objects in different clusters have great dissimilarity.
From a statistical point of view, cluster analysis is a method of simplifying data by data modeling. The traditional statistical clustering analysis method comprises a systematic clustering method, a decomposition method, an addition method, a dynamic clustering method, ordered sample clustering, overlapped clustering, fuzzy clustering and the like. Clustering analysis tools using k-means, k-centroids, etc. algorithms have been incorporated into many well-known statistical analysis software packages, such as SPSS, SAS, etc.
From a machine learning perspective, clusters correspond to hidden patterns. Clustering is an unsupervised learning process of searching clusters. Unlike classification, unsupervised learning does not rely on predefined classes or class-labeled training instances, requiring that the labels be automatically determined by a cluster learning algorithm, whereas class-learned instances or data objects have class labels. Clustering is an observed learning, not an example learning.
The cluster analysis is an exploratory analysis, and in the classification process, people do not need to give a classification standard in advance, and the cluster analysis can automatically classify from sample data. Different conclusions are often reached from the different methods used for cluster analysis. Different researchers do not necessarily obtain the same cluster number when performing cluster analysis on the same group of data.
From a practical application perspective, cluster analysis is one of the main tasks of data mining. And clustering can be used as an independent tool to obtain the distribution condition of data, observe the characteristics of each cluster of data, and intensively analyze a specific cluster set. Clustering analysis can also be used as a pre-processing step for other algorithms such as classification and qualitative induction algorithms.
Example 8
On the basis of the above embodiment, the data analysis unit, the method for establishing the data affinity between each dimension label in the multi-dimension label set, performs the following steps: setting the distance transformation function of each dimension label as:
where D (p, q) represents a set of Euclidean distances for each dimension label, dimension label Da(p) ordinate, D, of dimension labelb(q) denotes the abscissa of the dimension label, IbRepresenting a range value of the abscissa, wherein the value range is { 2-12 }; h isbAnd the range value of the vertical coordinate is { 3-15 }.
Example 9
On the basis of the previous embodiment, the method for analyzing the normalized data affinity between each dimension label corresponding to each examinee data and each dimension label corresponding to adjacent examinee data according to the random examination arrangement result by the data processing unit executes the following steps: setting the coordinates of target examinee data as follows: (x'n，y′n) (ii) a Regarding the point as a point in the chaotic system, and obtaining the chaotic mapping equation of the point as follows:
and then obtaining a sequential equation of the target examinee data and other adjacent examinee data for analysis and calculation through the following formula:
Example 10
On the basis of the above embodiment, the method of the data processing unit traversing the entire random examination arrangement results performs the following steps: setting a violence traversal rule of the random examination arrangement set, taking the violence traversal rule and the random examination arrangement set as data sources of a traversal algorithm, and constructing numbers in the password set by using the traversal algorithm to generate a final traversal random examination arrangement set; the random examination arrangement set comprises a first traversal digital table and a second traversal digital table, wherein the first traversal digital table and the second traversal digital table are 13 rows and 4 columns, the first traversal digital table stores the simply generated numbers according to the random examination arrangement set rule, the second traversal digital table stores the generated numbers according to the random examination arrangement set rule, the 1 st row in the first traversal digital table and the 1 st row in the second traversal digital table are both stored, and the 1 st row to the 4 th row are both empty and the 1 st is stored according to the random examination arrangement set digital rule.
The above description is only an embodiment of the present invention, but not intended to limit the scope of the present invention, and any structural changes made according to the present invention should be considered as being limited within the scope of the present invention without departing from the spirit of the present invention.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process and related description of the system described above may refer to the corresponding process in the foregoing method embodiments, and will not be described herein again.
It should be noted that, the system provided in the foregoing embodiment is only illustrated by dividing the functional modules, and in practical applications, the functions may be allocated to different functional modules according to needs, that is, the modules or steps in the embodiment of the present invention are further decomposed or combined, for example, the modules in the foregoing embodiment may be combined into one module, or may be further decomposed into multiple sub-modules, so as to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the modules or steps, and are not to be construed as unduly limiting the present invention.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes and related descriptions of the storage device and the processing device described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Those of skill in the art would appreciate that the various illustrative modules, method steps, and programs described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that such software modules, method steps, and corresponding programs may be located in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. To clearly illustrate this interchangeability of electronic hardware and software, various illustrative components and steps have been described above generally in terms of their functionality. Whether these functions are performed as electronic hardware or software depends upon the particular application and design constraints imposed on the technical solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The terms "first," "second," and the like are used for distinguishing between similar elements and not necessarily for describing or implying a particular order or sequence.
The terms "comprises," "comprising," or any other similar term are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention.
Claims (10)
1. A system for scheduling examinations based on multidimensional data analysis, comprising: the data acquisition unit is used for acquiring examinee data; the data analysis unit is used for carrying out multi-dimensional analysis on the examinee data, establishing a multi-dimensional label set aiming at each examinee data and simultaneously establishing data affinity among each dimension label in the multi-dimensional label set; the examination arrangement unit is used for carrying out random examination arrangement on the examinee data; the data processing unit is used for analyzing the normalized data affinity between each dimension label corresponding to each examinee data and each dimension label corresponding to adjacent examinee data according to the random examination arrangement result, adjusting the examinees with the data affinity higher than a set threshold value, and traversing the whole random examination arrangement result until the data affinity between each examinee data and the adjacent examinee data is below the set threshold value; the method is characterized in that the data analysis unit carries out multidimensional analysis on the examinee data, and the method for establishing the multidimensional label set aiming at each examinee data executes the following steps: dividing the data of each examinee into t dimensions, and calculating the clustering center of the data of each examinee according to the following formula:
wherein t is the number of dimensions, c is the number of clusters, N is the total number of samples, UtRepresenting a membership matrix, V, in the t-th dimensiontDenotes the cluster center in the t-th dimension, XtRepresents the t-th dimensionally small cluster sample,
represents the center point of the ith class in the t-th dimension, d is the dimension number of the sample, xj，tDenotes the jth sample point, μ, in the t dimensionij，tRepresenting the membership degree of the jth sample belonging to the ith class under the t dimensionality, wherein m is an adjustment coefficient and must satisfy m<1: based on the established center of the cluster, the cluster center,
is a clustering center; calculating the distance between each dimension label and the clustering center through the established clustering center; obtaining a coordinate point of the dimension label according to the calculated distance; and forming a set by all the obtained coordinate points to be used as a multi-dimensional label set.
2. The system of claim 1, wherein the data analysis unit, the method of establishing data affinity between each dimension tag in the multi-dimension tag set, performs the steps of: setting the distance transformation function of each dimension label as:
where D (p, q) represents a set of Euclidean distances for each dimension label, dimension label Da(p) ordinate, D, of dimension labelb(q) denotes the abscissa of the dimension label, IbRepresenting a range value of the abscissa, wherein the value range is { 2-12 }; and the range value of the hb position ordinate is { 3-15 }.
3. The system of claim 2, wherein the data processing unit analyzes each test based on the random test schedule resultsThe method for normalizing data affinity between each dimension label corresponding to each examinee data and each dimension label corresponding to adjacent examinee data comprises the following steps: setting the coordinates of target examinee data as follows: (x'n，y′n) (ii) a Regarding the point as a point in the chaotic system, and obtaining a chaotic mapping equation of the point as follows:
and then obtaining a sequential equation of the target examinee data and other adjacent examinee data for analysis and calculation through the following formula:
wherein x isnAnd ynThe abscissa and ordinate of the examinee data for the next analytical calculation; x'nAnd y'nThe abscissa and the ordinate of the examinee data subjected to analysis and calculation at present are taken as the reference; a is a first adjustment parameter, and the range is: (1-5); c is a first adjustment coefficient in the range of (1.1-1.5); b is a second adjustment parameter, the range is: (1-5); d is a second adjustment coefficient in the range of (1.1-1.5).
4. The system of claim 3, wherein the method of the data processing unit traversing the entire random test schedule results performs the steps of: setting a violence traversal rule of the random examination arrangement set, taking the violence traversal rule and the random examination arrangement set as data sources of a traversal algorithm, and constructing numbers in the password set by using the traversal algorithm to generate a final traversal random examination arrangement set; the random examination arrangement set comprises a first traversal digital table and a second traversal digital table, the first traversal digital table and the second traversal digital table are both 4 rows and 13 columns, the first traversal digital table stores the simply generated numbers according to the random examination arrangement set rule, the second traversal digital table stores the generated numbers according to the random examination arrangement set rule, the 1 st row in the first traversal digital table and the 1 st row in the second traversal digital table are both stored in a top grid mode, and the 2 nd row to the 4 th row are both stored in a blank mode according to the random examination arrangement set digital rule.
5. The system of claim 4, wherein the first traversal number table and the second traversal number table brute force traversal rules comprise: locking a certain row in the digital history table to traverse from left to right, from right to left, from left to right and back again, and from right to left and back again; traversing the rule from top to bottom, locking a certain column in the digital history table to traverse from top to bottom, from bottom to top, from top to bottom and back again, and from bottom to top and back again to the rule; the cross-row traversal rule is used for locking some rows in the digital traversal table to regularly traverse in the same direction and the opposite direction of each row; and traversing the rule in a cross-column manner, wherein a certain column in the locking number traversal table is traversed by the rule that each column is in the same direction and each column is in the opposite direction.
6. A method for multi-dimensional data analysis based scheduling of examinations according to the system of one of claims 1 to 5, characterized in that it performs the following steps:
step 1: step 1: used for collecting the examinee data;
step 2: carrying out multi-dimensional analysis on the examinee data, establishing a multi-dimensional label set aiming at each examinee data, and simultaneously establishing data affinity among each dimension label in the multi-dimensional label set;
and step 3: carrying out random examination arrangement on the examinee data;
and 4, step 4: and analyzing the normalized data affinity between each dimension label corresponding to each examinee data and each dimension label corresponding to the adjacent examinee data according to the random examination arrangement result, adjusting the examinees with the data affinity higher than a set threshold value, and traversing the whole random examination arrangement result until the data affinity between each examinee data and the adjacent examinee data is below the set threshold value.
7. The method of claim 6, wherein the data analysis unit performs multidimensional analysis on the test taker data, and the method of creating a multidimensional labelset for each test taker data performs the steps of: dividing the data of each examinee into t dimensions, and calculating the clustering center of the data of each examinee according to the following formula:
wherein t is the number of dimensions, c is the number of clusters, N is the total number of samples, UtRepresenting a membership matrix, V, in the t-th dimensiontDenotes the cluster center in the t-th dimension, XtRepresents the t-th dimensionally small cluster sample,
represents the center point of the ith class in the t-th dimension, d is the dimension number of the sample, xj，tDenotes the jth sample point, μ, in the t dimensionij，tRepresenting the membership degree of the jth sample belonging to the ith class under the t dimensionality, wherein m is an adjustment coefficient and must satisfy m<1: based on the established center of the cluster, the cluster center,
is a clustering center; calculating the distance between each dimension label and the clustering center through the established clustering center; obtaining a coordinate point of the dimension label according to the calculated distance; and forming a set by all the obtained coordinate points to be used as a multi-dimensional label set.
8. The method of claim 7, wherein the data analysis unit, the method of establishing data affinity between each dimension tag in the multi-dimension tag set, performs the steps of: setting the distance transformation function of each dimension label as:where D (p, q) represents a set of Euclidean distances for each dimension label, dimension label Da(p) ordinate, D, of dimension labelb(q) represents dimensionAbscissa of degree label, IbRepresenting a range value of the abscissa, wherein the value range is { 2-12 }; h isbAnd the range value of the vertical coordinate is { 3-15 }.
9. The method of claim 8, wherein the data processing unit, based on the random exam arrangement results, performs the following steps by analyzing normalized data affinity between each dimension label corresponding to each test taker data and each dimension label corresponding to adjacent test taker data: setting the coordinates of target examinee data as follows: x'n，y′n) (ii) a Regarding the point as a point in the chaotic system, and obtaining a chaotic mapping equation of the point as follows:
and then obtaining a sequential equation of the target examinee data and other adjacent examinee data for analysis and calculation through the following formula:
wherein x isnAnd ynThe abscissa and ordinate of the examinee data for the next analytical calculation; x'nAnd y'nThe abscissa and the ordinate of the examinee data subjected to analysis and calculation at present are taken as the reference; a is a first adjustment parameter, and the range is: (1-5); c is a first adjustment coefficient in the range of (1.1-1.5); b is a second adjustment parameter, the range is: (1-5); d is a second adjustment coefficient in the range of (1.1-1.5).
10. The method of claim 9, wherein the method of the data processing unit traversing the entire random test schedule results performs the steps of: setting a violence traversal rule of the random examination arrangement set, taking the violence traversal rule and the random examination arrangement set as data sources of a traversal algorithm, and constructing numbers in the password set by using the traversal algorithm to generate a final traversal random examination arrangement set; the random examination arrangement set comprises a first traversal digital table and a second traversal digital table, the first traversal digital table and the second traversal digital table are both 4 rows and 13 columns, the first traversal digital table stores the simply generated numbers according to the random examination arrangement set rule, the second traversal digital table stores the generated numbers according to the random examination arrangement set rule, the 1 st row in the first traversal digital table and the 1 st row in the second traversal digital table are both stored in a top grid mode, and the 2 nd row to the 4 th row are both stored in a blank mode according to the random examination arrangement set digital rule.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010058368.9A CN111274290B (en) | 2020-01-19 | 2020-01-19 | Examination arrangement system and method based on multidimensional data analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010058368.9A CN111274290B (en) | 2020-01-19 | 2020-01-19 | Examination arrangement system and method based on multidimensional data analysis |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111274290A true CN111274290A (en) | 2020-06-12 |
| CN111274290B CN111274290B (en) | 2021-10-26 |
Family
ID=71003094
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010058368.9A Active CN111274290B (en) | 2020-01-19 | 2020-01-19 | Examination arrangement system and method based on multidimensional data analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111274290B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111624931A (en) * | 2020-06-18 | 2020-09-04 | 山东山大世纪科技有限公司 | Industrial park electricity utilization internet intelligent operation and maintenance management and control system and method |
| CN112613562A (en) * | 2020-12-24 | 2021-04-06 | 山东鑫泰洋智能科技有限公司 | Data analysis system and method based on multi-center cloud computing |
| CN112634103A (en) * | 2020-12-31 | 2021-04-09 | 重庆臻善雅欣科技有限公司 | Integrated teaching system applied to smart campus |
| CN116542497A (en) * | 2023-07-06 | 2023-08-04 | 浙江海亮科技有限公司 | Examination room distribution method and device, electronic equipment and medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006318058A (en) * | 2005-05-10 | 2006-11-24 | Ic Brains Co Ltd | Seat management system |
| CN104732464A (en) * | 2015-02-27 | 2015-06-24 | 南京林业大学 | Standardization examination examinee identity recognition and seating arrangement self-service terminal system |
| CN105160611A (en) * | 2015-07-13 | 2015-12-16 | 江苏省教育考试院 | Large-scale examination room seat arrangement system |
| CN109816251A (en) * | 2019-01-29 | 2019-05-28 | 广州番禺职业技术学院 | A kind of arrangement of the tests method and system based on university's autonomous enrollment platform |
-
2020
- 2020-01-19 CN CN202010058368.9A patent/CN111274290B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006318058A (en) * | 2005-05-10 | 2006-11-24 | Ic Brains Co Ltd | Seat management system |
| CN104732464A (en) * | 2015-02-27 | 2015-06-24 | 南京林业大学 | Standardization examination examinee identity recognition and seating arrangement self-service terminal system |
| CN105160611A (en) * | 2015-07-13 | 2015-12-16 | 江苏省教育考试院 | Large-scale examination room seat arrangement system |
| CN109816251A (en) * | 2019-01-29 | 2019-05-28 | 广州番禺职业技术学院 | A kind of arrangement of the tests method and system based on university's autonomous enrollment platform |
Non-Patent Citations (1)
| Title |
|---|
| 王波伟: "模糊聚类算法研究及其应用", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111624931A (en) * | 2020-06-18 | 2020-09-04 | 山东山大世纪科技有限公司 | Industrial park electricity utilization internet intelligent operation and maintenance management and control system and method |
| CN112613562A (en) * | 2020-12-24 | 2021-04-06 | 山东鑫泰洋智能科技有限公司 | Data analysis system and method based on multi-center cloud computing |
| CN112613562B (en) * | 2020-12-24 | 2023-05-12 | 广州禧闻信息技术有限公司 | Data analysis system and method based on multi-center cloud computing |
| CN112634103A (en) * | 2020-12-31 | 2021-04-09 | 重庆臻善雅欣科技有限公司 | Integrated teaching system applied to smart campus |
| CN116542497A (en) * | 2023-07-06 | 2023-08-04 | 浙江海亮科技有限公司 | Examination room distribution method and device, electronic equipment and medium |
| CN116542497B (en) * | 2023-07-06 | 2023-10-13 | 浙江海亮科技有限公司 | Examination room distribution method and device, electronic equipment and medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111274290B (en) | 2021-10-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111274290B (en) | Examination arrangement system and method based on multidimensional data analysis | |
| Nosofsky | The generalized context model: An exemplar model of classification | |
| Mu et al. | A scale-space clustering method: Mitigating the effect of scale in the analysis of zone-based data | |
| García et al. | On first quartile journals which are not of highest impact | |
| Murray et al. | Explainable ai for the choquet integral | |
| Delgado et al. | Analysis of students’ behavior through user clustering in online learning settings, based on self organizing maps neural networks | |
| CN112163161B (en) | Recommendation method and system for college library, readable storage medium and electronic equipment | |
| Xu et al. | Effective community division based on improved spectral clustering | |
| González-Vidal et al. | A transfer learning framework for predictive energy-related scenarios in smart buildings | |
| Al-Rasheed | Identification of important features and data mining classification techniques in predicting employee absenteeism at work. | |
| Rabbi et al. | An Approximation For Monitoring The Efficiency Of Cooperative Across Diverse Network Aspects | |
| Loganathan et al. | Development of machine learning based framework for classification and prediction of students in virtual classroom environment | |
| Ruvolo et al. | Exploiting commonality and interaction effects in crowdsourcing tasks using latent factor models | |
| Rodrigues et al. | Estimating disaggregated employment size from points-of-interest and census data: From mining the web to model implementation and visualization | |
| Sarmento et al. | An overview of statistical data analysis | |
| Wang et al. | SpecVAT: Enhanced visual cluster analysis | |
| Khan et al. | Using web log files the comparative study of Big Data with map reduce technique | |
| Papayiannis et al. | On clustering uncertain and structured data with Wasserstein barycenters and a geodesic criterion for the number of clusters | |
| Atiku et al. | Machine learning classification techniques for detecting the impact of human resources outcomes on commercial banks performance | |
| WO1992017853A2 (en) | Direct data base analysis, forecasting and diagnosis method | |
| Mahatme et al. | Data mining with fuzzy method towards intelligent questions categorization in E-Learning | |
| Martí et al. | Anomaly detection with the voronoi diagram evolutionary algorithm | |
| Mirkin et al. | Building Fuzzy Thematic Clusters and Mapping Them to Higher Ranks in a Taxonomy. | |
| Honaker et al. | Sorting Algorithms for Qualitative Data to Recover Latent Dimensions with Crowdsourced Judgments: Measuring State Policies for Welfare Eligibility under TANF | |
| Han et al. | Synchronization-Inspired Interpretable Neural Networks |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |